.png){kind=icon}
Return
Security and Safety-Critical Learning-Based Collaborative Control for Multiagent Systems
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2024.3350679.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This article presents a novel learning-based collaborative control framework to ensure communication security and formation safety of nonlinear multiagent systems (MASs) subject to denial-of-service (DoS) attacks, model uncertainties, and barriers in environments. The framework has a distributed and decoupled design at the cyber-layer and the physical layer. A resilient control Lyapunov function-quadratic programming (RCLF-QP)-based observer is first proposed to achieve secure reference state estimation under DoS attacks at the cyber-layer. Based on deep reinforcement learning (RL) and control barrier function (CBF), a safety-critical formation controller is designed at the physical layer to ensure safe collaborations between uncertain agents in dynamic environments. The framework is applied to autonomous vehicles for area scanning formations with barriers in environments. The comparative experimental results demonstrate that the proposed framework can effectively improve the resilience and robustness of the system.
Keywords:
Security
Collaboration
Safety
Denial-of-service attack
Vehicle dynamics
Uncertainty
Task analysis
Denial-of-service (DoS) attacks
learning-based control
multiagent systems (MASs)
safety-critical formation control
Journal
IF:
8.9
Papers:
7.6K
Citations:
7.2W
Organization
View more organizations
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSaveShareSaveShareSaveShareSaveShareSave
View more